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Cited by 579

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Topaz-Denoise: general deep denoising models for cryoEM and cryoET

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Bepler, Tristan, Kelley, Kotaro, Noble, Alex J., Berger, Bonnie·2020·Nature Communications
DOI 10.1038/s41467-020-18952-1

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Abstract

Cryo-electron microscopy (cryoEM) is becoming the preferred method for resolving protein structures. Low signal-to-noise ratio (SNR) in cryoEM images reduces the confidence and throughput of structure determination during several steps of data processing, resulting in impediments such as missing particle orientations. Denoising cryoEM images can not only improve downstream analysis but also accelerate the time-consuming data collection process by allowing lower electron dose micrographs to be used for analysis. Here, we present Topaz-Denoise, a deep learning method for reliably and rapidly increasing the SNR of cryoEM images and cryoET tomograms. By training on a dataset composed of thousands of micrographs collected across a wide range of imaging conditions, we are able to learn models capturing the complexity of the cryoEM image formation process. The general model we present is able to denoise new datasets without additional training. Denoising with this model improves micrograph interpretability and allows us to solve 3D single particle structures of clustered protocadherin, an elongated particle with previously elusive views. We then show that low dose collection, enabled by Topaz-Denoise, improves downstream analysis in addition to reducing data collection time. We also present a general 3D denoising model for cryoET. Topaz-Denoise and pre-trained general models are now included in Topaz. We expect that Topaz-Denoise will be of broad utility to the cryoEM community for improving micrograph and tomogram interpretability and accelerating analysis.

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Cited by

Metadata

Authors
Bepler, T., Kelley, K., Noble, A. J., & Berger, B.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2020-10-15
Volume
11
Issue
1
Pages
5208-5208
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-020-18952-1
OpenAlex
W3093295276
Cited by
579
References
4
FWCI
25.50
Type
journal article
Language
en
Open access
gold
Licence
Funders
U.S. Department of Health & Human Services | NIH | Office of Extramural Research, National Institutes of Health (GM081871)

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